title: Comparing metabolic energy expenditure estimation using wearable multi-sensor network and single accelerometer.
abstract: This paper presents the implementation details, system architecture and performance of a wearable sensor network that was designed for human activity recognition and energy expenditure estimation. We also included ActiGraph GT3X+ as a popular single sensor solution for detailed comparison with the proposed wearable sensor network. Linear regression and Artificial Neural Network are implemented and tested. Through a rigorous system study and experiment, it is shown that the wearable multi-sensor network outperforms the single sensor solution in terms of energy expenditure estimation.
authors.forename: Bo
authors.initials: B
authors.lastname: Dong
authors.forename: Subir
authors.initials: S
authors.lastname: Biswas
authors.forename: Alexander
authors.initials: A
authors.lastname: Montoye
authors.forename: Karin
authors.initials: K
authors.lastname: Pfeiffer
mesh_terms: D000054:Acceleration; D000328:Adult; D004734:Energy Metabolism; D004867:Equipment Design; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D016571:Neural Networks, Computer; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted; D013686:Telemetry / Q000295:instrumentation*; D055815:Young Adult